RNA markers enable phenotypic test of antibiotic susceptibility in Neisseria gonorrhoeae after 10 minutes of ciprofloxacin exposure
Bibliographic record
Abstract
Antimicrobial-resistant Neisseria gonorrhoeae is an urgent public-health threat, with continued worldwide incidents of infection and rising resistance to antimicrobials. Traditional culture-based methods for antibiotic susceptibility testing are unacceptably slow (1-2 days), resulting in the use of broad-spectrum antibiotics and the further development and spread of resistance. Critically needed is a rapid antibiotic susceptibility test (AST) that can guide treatment at the point-of-care. Rapid phenotypic approaches using quantification of DNA have been demonstrated for fast-growing organisms (e.g. E. coli) but are challenging for slower-growing pathogens such as N. gonorrhoeae. Here, we investigate the potential of RNA signatures to provide phenotypic responses to antibiotics in N. gonorrhoeae that are faster and greater in magnitude compared with DNA. Using RNA sequencing, we identified antibiotic-responsive transcripts. Significant shifts (>4-fold change) in transcript levels occurred within 5 min of antibiotic exposure. We designed assays for responsive transcripts with the highest abundances and fold changes, and validated gene expression using digital PCR. Using the top two markers (porB and rpmB) we correctly determined the antibiotic susceptibility and resistance of 49 clinical isolates after 10 min exposure to ciprofloxacin. RNA signatures are therefore promising as an approach on which to build rapid AST devices for N. gonorrhoeae at the point-of-care, which is critical for disease management, surveillance, and antibiotic stewardship efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".